Senior Data Scientist at Quest Analytics

Job description

At Quest Analytics, the models and insights our Data Science team delivers help solve real problems around the accuracy, adequacy, and accessibility of healthcare provider networks. We're looking for a Senior Data Scientist to join our team. In this role, you'll leverage complex healthcare data to drive insights, improve data quality, and enable product innovation. You'll develop scalable machine learning models and AI-driven approaches to solve high-impact business problems for health plan networks, and help shape how Quest Analytics continues to evolve its data science capabilities. This is a senior individual contributor role with significant opportunity to influence our products, technical approaches, and data strategy, including how Quest Analytics uses machine learning, advanced analytics, and emerging AI technologies. If you're someone who likes digging into complicated datasets, challenging assumptions, experimenting with new approaches, and ultimately turning that work into scalable solutions, we'd love to talk with you. What You'll Do: You'll partner across Data, Product, Engineering, and Client teams to: Design, develop, optimize, and scale machine learning models and advanced analytics solutions to improve provider data quality and generate meaningful network insights Apply statistical modeling, predictive analytics, and machine learning techniques (classification, regression, clustering, NLP, anomaly detection) to solve business problems Research open-ended healthcare and provider-data challenges, form hypotheses, and develop action plans and analytical approaches to turn findings into actionable solutions Develop novel algorithms to solve complex or unprecedented problems Evaluate and improve existing models and algorithms to increase accuracy, performance, scalability, and reliability, and measure the resulting business impact Perform large-scale data processing and analysis using Python, SQL, Databricks, Spark, and distributed computing across structured and unstructured datasets Develop and maintain automated systems for anomaly detection, data validation, feature engineering, and continuous model monitoring Design feature engineering strategies and evaluate model performance using appropriate validation techniques Evaluate and integrate third-party data sources, tools, and vendors to strengthen our datasets and analytical capabilities Partner with Engineering to productionize models and build scalable, reliable solutions within our software products Apply LLM tools such as Claude to accelerate analysis, automate workflows, and improve documentation, and explore opportunities in generative AI, NLP, and AI agents Develop and maintain metrics to evaluate model performance, data quality, and business impact Translate complex findings into clear, actionable insights for technical and non-technical stakeholders Present findings and recommendations to Network Analytics leadership and broader company audiences Partner with Product and business teams to identify opportunities for new features, metrics, and data-driven solutions Mentor other data scientists and analysts, and contribute to technical standards, data governance, and MLOps best practices Participate in cross-functional initiatives and support client-facing analytics discussions as needed What You'll Need: 7+ years of experience in data science, machine learning, or advanced analytics, preferably in a SaaS organization Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related discipline, or equivalent relevant experience Demonstrated experience building and delivering machine learning models into production Advanced Python skills, including tools such as pandas, NumPy, and scikit-learn Strong experience with Databricks, Spark, or comparable distributed computing technologies Advanced SQL skills Experience with ML frameworks such as TensorFlow, PyTorch, or similar technologies Strong understanding of machine learning techniques including classification, regression, clustering, NLP, and anomaly detection Strong applied statistics knowledge, including hypothesis testing, regression analysis, and probability Experience working with large, messy, and complex datasets Understanding of MLOps, including deployment, monitoring, validation, and model lifecycle management Experience using LLMs and AI-assisted tools to improve data science workflows Ability to independently tackle ambiguous problems and determine an effective path forward Ability to explain complex technical findings in a way that creates clear business value Strong collaboration skills and experience working across Product, Engineering, Data, and business stakeholders Experience leading technical initiatives and mentoring others Experience in one or more of these areas would be especially valuable: Healthcare provider data, provider networks, or healthcare analytics Healthcare, SaaS, or another highly regulated data environment Integrating ML models into production software products Data quality, entity resolution, anomaly detection, or large-scale data validation Generative AI, NLP, LLM applications, or agentic workflows Data visualization and communicating analytical results to clients or executive audiences About Quest Analytics For more than 20 years, we’ve been improving provider network management one groundbreaking innovation at a time. 90% of America’s health plans use our tools, including the eight largest in the nation. Achieve your personal quest to build a great career here. What you’ll appreciate: •Workplace flexibility – you choose between remote, hybrid or in-office •Affordable employee medical, dental and vision plans with low employee cost •401k Matching •Wellness benefits •Competitive salary and success sharing bonus •Flexible vacation, plus sick time and holidays •An entrepreneurial culture that won’t limit you to a job description •Being listened to, valued, appreciated -- and having your contributions rewarded •Enjoying your work each day with a great group of people Apply TODAY! careers.questanalytics.com Next Steps After you apply, you will receive an email confirming receipt of your application. If your background and experience align with our needs, a member of the Quest Analytics Talent Acquisition team will contact you regarding next steps in the process. Candidates will receive communication throughout the interview process regarding their status and progression. Interview Process The interview process may vary by role but typically includes: Recruiter Video Interview Video Interview with the Hiring Manager Team and/or Panel Interview(s) Role-Specific Assessment, Presentation, or Case Study (if applicable) Professional Reference Checks via Skill Survey Final Interview with Executive Leadership Video Camera Usage Policy Quest Analytics is a remote-first organization that values collaboration, communication, and engagement. To support a connected and productive work environment, candidates are expected to participate in all virtual interviews with their cameras enabled. Video participation helps create a more interactive interview experience, allows for stronger communication and relationship-building, and supports our commitment to maintaining a secure hiring process. Camera usage also serves as an important safeguard against fraudulent applicants, identity misrepresentation, unauthorized participation, and other security risks that have become increasingly common in remote hiring environments. Candidates who are unable or unwilling to participate on camera during the interview process may not be considered for employment. Relocation Requirement While many positions at Quest Analytics are remote, employees are expected to reside in and perform work from the location disclosed during the hiring process. Any relocation must be communicated to and approved by Quest Analytics in advance. This policy helps ensure compliance with employment and tax regulations, protects company and client data, and reduces risks associated with unauthorized work arrangements and employment misrepresentation. AI Tools As part of our hiring process, Quest Analytics may use AI-assisted tools to help screen applications and support interview documentation, including resume-matching software and AI-generated interview summaries. These tools inform but do not replace human judgment; all hiring decisions are made by Quest Analytics team members. If you have questions about our use of AI in hiring, please contact recruiting@questanalytics.com . **Visa sponsorship is not available at this time. Preferred work locations are within one of the following states: Alabama, Arizona, Arkansas, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, Nevada, New Hampshire, New Mexico, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, West Virginia, Wisconsin, or Wyoming. Quest Analytics provides equal employment opportunities to all people without regard to race, color, religion, sex, national origin, ancestry, marital status, veteran status, age, disability, sexual orientation or gender identity or expression or any other legally protected category. We are committed to creating and maintaining a workforce environment that is free from any form of discriminations or harassment. Applicants must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire. Persons with disabilities who anticipate needing accommodations for any part of the application process may contact, in confidence hr@questanalytics.com NOTE: Staffing agencies, headhunters, recruiters, and/or placement agencies, please do not contact our hiring managers directly. We are not currently working with additional outside agencies at this time. Any job posting displayed on websites other than questanalytics.com or jobs.lever.co/questanalytics/ may be out of date, inaccurate and unavailable RECRUITMENT FRAUD ALERT We understand that some individuals have recently been impersonating recruiters from Quest Analytics® and soliciting fake job opportunities. We urge you not to disclose any personal or financial information to them. We use only our official website ( questanalytics.com ), email domain (@questanalytics.com), and verified social media accounts to communicate with candidates. If you suspect fraudulent behavior, please report it to us at infosec@questanalytics.com . We appreciate your interest in working with us. To learn more: questanalytics.com

Requirements

Must-haves

At Quest Analytics, the models and insights our Data Science team delivers help solve real problems around the accuracy, adequacy, and accessibility of healthcare provider networks.
If you're someone who likes digging into complicated datasets, challenging assumptions, experimenting with new approaches, and ultimately turning that work into scalable solutions, we'd love to talk with you.
You'll partner across Data, Product, Engineering, and Client teams to:
Design, develop, optimize, and scale machine learning models and advanced analytics solutions to improve provider data quality and generate meaningful network insights
Apply statistical modeling, predictive analytics, and machine learning techniques (classification, regression, clustering, NLP, anomaly detection) to solve business problems
Research open-ended healthcare and provider-data challenges, form hypotheses, and develop action plans and analytical approaches to turn findings into actionable solutions
Develop novel algorithms to solve complex or unprecedented problems
Evaluate and improve existing models and algorithms to increase accuracy, performance, scalability, and reliability, and measure the resulting business impact
Perform large-scale data processing and analysis using Python, SQL, Databricks, Spark, and distributed computing across structured and unstructured datasets
Develop and maintain automated systems for anomaly detection, data validation, feature engineering, and continuous model monitoring
Design feature engineering strategies and evaluate model performance using appropriate validation techniques
Evaluate and integrate third-party data sources, tools, and vendors to strengthen our datasets and analytical capabilities
Partner with Engineering to productionize models and build scalable, reliable solutions within our software products
Apply LLM tools such as Claude to accelerate analysis, automate workflows, and improve documentation, and explore opportunities in generative AI, NLP, and AI agents
Develop and maintain metrics to evaluate model performance, data quality, and business impact
Translate complex findings into clear, actionable insights for technical and non-technical stakeholders
Present findings and recommendations to Network Analytics leadership and broader company audiences
Partner with Product and business teams to identify opportunities for new features, metrics, and data-driven solutions
Mentor other data scientists and analysts, and contribute to technical standards, data governance, and MLOps best practices
Participate in cross-functional initiatives and support client-facing analytics discussions as needed
7+ years of experience in data science, machine learning, or advanced analytics, preferably in a SaaS organization
Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related discipline, or equivalent relevant experience
Demonstrated experience building and delivering machine learning models into production

Nice-to-haves

Competitive salary and success sharing bonus

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

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Platform: lever
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Wellness